Retail Sales Analysis SQL Project

Project Overview

Project Title: Retail Sales Analysis
Level: Beginner
Database: RETAIL_MART_SALES

This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries. This project is ideal for those who are starting their journey in data analysis and want to build a solid foundation in SQL.

Objectives

1. Set up a retail sales database: Create and populate a retail sales database with the provided sales data.
2. Data Cleaning: Identify and remove any records with missing or null values.
3. Exploratory Data Analysis (EDA): Perform basic exploratory data analysis to understand the dataset.
4. Business Analysis: Use SQL to answer specific business questions and derive insights from the sales data.

Project Structure

1. Database Setup

• Database Creation: The project starts by creating a database named RETAIL_MART_SALES.
• Table Creation: A table named sales is created to store the sales data. The table structure includes columns for transaction ID, sale date, sale time, customer ID, gender, age, product category, quantity sold, price per unit, cost of goods sold (COGS), and total sale amount.

CREATE DATABASE RETAIL_MART_SALES;

CREATE TABLE sales
(
    transactions_id INT PRIMARY KEY,
    sale_date DATE,	
    sale_time TIME,
    customer_id INT,	
    gender VARCHAR(10),
    age INT,
    category VARCHAR(35),
    quantity INT,
    price_per_unit FLOAT,	
    cogs FLOAT,
    total_sale FLOAT
);

2. Data Exploration & Cleaning

• Record Count: Determine the total number of records in the dataset.
• Customer Count: Find out how many unique customers are in the dataset.
• Category Count: Identify all unique product categories in the dataset.
• Null Value Check: Check for any null values in the dataset and delete records with missing data.

SELECT COUNT(*) FROM sales;
SELECT COUNT(DISTINCT customer_id) FROM sales;
SELECT DISTINCT category FROM sales;

SELECT * FROM sales
WHERE 
    sale_date IS NULL OR sale_time IS NULL OR customer_id IS NULL OR 
    gender IS NULL OR age IS NULL OR category IS NULL OR 
    quantity IS NULL OR price_per_unit IS NULL OR cogs IS NULL;

DELETE FROM sales
WHERE 
    sale_date IS NULL OR sale_time IS NULL OR customer_id IS NULL OR 
    gender IS NULL OR age IS NULL OR category IS NULL OR 
    quantity IS NULL OR price_per_unit IS NULL OR cogs IS NULL;

3. Data Analysis & Findings

The following SQL queries were developed to answer specific business questions:

1. ** How many sales do we have?**:

SELECT count(*) from sales

2. Write a SQL query to retrieve all transactions where the category is 'Clothing' and the quantity sold is more than 4 in the month of Nov-2022:

SELECT 
    *
FROM
    sales
WHERE
    category = 'Clothing' AND quantity >= 4
        AND YEAR(sale_date) = '2022'
        AND MONTH(sale_date) = '11';

3. Write a SQL query to calculate the total sales (total_sale) for each category.:

SELECT 
    category, SUM(total_sale)
FROM
    sales
GROUP BY sales.category

4. Write a SQL query to find the average age of customers who purchased items from the 'Beauty' category.:

SELECT AVG(age) as avg_age from sales 
WHERE category = 'Beauty' ;

5. Write a SQL query to find all transactions where the total_sale is greater than 1000.:

SELECT
* FROM sales
WHERE
 total_sale > 1000

6. Write a SQL query to find the total number of transactions (transaction_id) made by each gender in each category.:

SELECT 
    category, gender, COUNT(transactions_id)
FROM
    sales
GROUP BY category , gender
ORDER BY category DESC;

7. Write a SQL query to calculate the average sale for each month:

SELECT MONTH(sale_date) as month_, AVG(total_sale) as avg_sale FROM sales
GROUP BY MONTH(sale_date) ORDER BY MONTH(sale_date) desc ;

8. **Write a SQL query to find the top 5 customers based on the highest total sales **:

SELECT customer_id , SUM(total_sale) AS sales_no FROM sales
GROUP BY customer_id  
ORDER BY sales_no DESC LIMIT 5 ;

9. Write a SQL query to find the number of unique customers who purchased items from each category.:

SELECT 
    category,    
    COUNT(DISTINCT customer_id) as cnt_unique_cs
FROM sales
GROUP BY category

10. Write a SQL query to create each shift and number of orders (Example Morning <12, Afternoon Between 12 & 17, Evening >17):

WITH hourly_sale AS (
    SELECT *,
        CASE
            WHEN HOUR(sale_time) < 12 THEN 'Morning'
            WHEN HOUR(sale_time) BETWEEN 12 AND 17 THEN 'Afternoon'
            ELSE 'Evening'
        END AS shift
    FROM retail_sales
)
SELECT 
    shift,
    COUNT(*) AS total_orders    
FROM hourly_sale
GROUP BY shift;

Findings

• Customer Demographics: The dataset includes customers from various age groups, with sales distributed across different categories such as Clothing and Beauty.
• High-Value Transactions: Several transactions had a total sale amount greater than 1000, indicating premium purchases.
• Sales Trends: Monthly analysis shows variations in sales, helping identify peak seasons.
• Customer Insights: The analysis identifies the top-spending customers and the most popular product categories.

Reports

• Sales Summary: A detailed report summarizing total sales, customer demographics, and category performance.
• Trend Analysis: Insights into sales trends across different months and shifts.
• Customer Insights: Reports on top customers and unique customer counts per category.

Conclusion

This project serves as a comprehensive introduction to SQL for data analysts, covering database setup, data cleaning, exploratory data analysis, and business-driven SQL queries. The findings from this project can help drive business decisions by understanding sales patterns, customer behavior, and product performance.

Author - HARSHAL DHOTE

This project is part of my portfolio, showcasing the SQL skills essential for data analyst roles.